agentleFS
Sign inStart free
SubliminalCodingSubliminalCoding/bilbroswagginz-skills/skills/qwen-bug-zero/SKILL.md

qwen-bug-zero

Skill0 starsChanged 46 days agoMITView on GitHub

Local-Qwen bug pass (token-free). Mechanical form of /bug-zero for a small local model: run the bug finders, CONFIRM each finding against the real code before fixing (skip if unsure; don't fix phantom bugs), fix one at a time with a regression test under build→tests→lint, rescan until two passes find nothing real. Use when you want /bug-zero's job done on a local model with no API tokens: hardening a project.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Never weaken or delete a test to clear a finding.

    line 53
  2. Distinctive:

    If a "bug" is actually a design/scope question → DECISIONS.md, not a fix.

    line 55

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

qwen-bug-zero (local correctness)

The Qwen-run version of /bug-zero. The one judgment it must make ("is this finding real?") is handled by a hard rule: confirm in the code, and when unsure, SKIP (a phantom bug fixed is worse than one left).

QWEN LOCAL RULES (full text: ../qwen-forge/references/QWEN-MODE.md)

Requires qwen-forge installed alongside this skill (it holds the shared rules).

  • Builder, not architect. ONE change at a time; build → tests → lint; green → commit; red → revert, one retry, else STOP. Never weaken tests.
  • Unclear / judgment / outward → DECISIONS.md + STOP.
  • Local endpoint required: a local OpenAI-compatible endpoint (vLLM, SGLang, llama.cpp or Ollama) serving a capable Qwen-class coding model. ONE model loaded, work SEQUENTIALLY, leave memory headroom on unified-memory machines.

Steps

1. SWEEP. Run the finders that fit this project, ONE at a time (sequential, one GPU):
     - the test suite + lint   (real failures; always available, the baseline)
     - ubs / ubs-workflow      (general bugs; if installed)
     - mock-code-finder        (stubs/fakes in live paths; if installed)
     - deadlock-finder-and-fixer (only if there's concurrency; if installed)
   Collect every finding into a list. FIX NOTHING YET.

2. CONFIRM (the important step). For each finding: open the cited file/line and check it
   is a REAL, reachable bug.
     - real      → keep it.
     - not sure  → SKIP it, note in DECISIONS.md. Do NOT fix it.
   Never fix a bug you cannot see in the actual code.

3. FIX. Confirmed bugs, ONE at a time:
     - make the smallest fix.
     - add a regression test that would have caught it.
     - build → tests → lint. green → commit. red → revert, one retry, else STOP.

4. RESCAN. Re-run the finders. Repeat steps 2–4 until two passes in a row find nothing
   real. Then report.

Rules

  • Never weaken or delete a test to clear a finding.
  • One fix at a time; the regression test per fix is mandatory (it keeps the bug dead).
  • If a "bug" is actually a design/scope question → DECISIONS.md, not a fix.

Distilled from /bug-zero. Its ROUTING.md holds the full finder roster and the smart-model adversarial-verify version for a Claude/human driver.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free